The potential for AI to revolutionize conservation: a horizon scan

Reynolds, Sam A.; Beery, Sara; Burgess, Neil; Burgman, Mark; Butchart, Stuart H.M.; Cooke, Steven J.; Coomes, David; Danielsen, Finn; Di Minin, Enrico; Durán, América Paz; Gassert, Francis; Hinsley, Amy; Jaffer, Sadiq; Jones, Julia P.G.; Li, Binbin V.; Mac Aodha, Oisin; Madhavapeddy, Anil; O’Donnell, Stephanie A.L.; Oxbury, William M.; Peck, Lloyd ORCID: https://orcid.org/0000-0003-3479-6791; Pettorelli, Nathalie; Rodriguez, Jon Paul; Shuckburgh, Emily; Strassburg, Bernardo; Yamashita, Hiromi; Miao, Zhongqi; Sutherland, William J.. 2024 The potential for AI to revolutionize conservation: a horizon scan. Trends in Ecology & Evolution, 3384. 17, pp. 10.1016/j.tree.2024.11.013 (In Press)

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Official URL: https://doi.org/10.1016/j.tree.2024.11.013

Abstract/Summary

Artificial Intelligence (AI) is an emerging tool that could be leveraged to identify the effective conservation solutions demanded by the urgent biodiversity crisis. We present the results of our horizon scan of AI applications likely to significantly benefit biological conservation. An international panel of conservation scientists and AI experts identified 21 key ideas. These included species recognition to uncover 'dark diversity', multimodal models to improve biodiversity loss predictions, monitoring wildlife trade, and addressing human–wildlife conflict. We consider the potential negative impacts of AI adoption, such as AI colonialism and loss of essential conservation skills, and suggest how the conservation field might adapt to harness the benefits of AI while mitigating its risks.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.tree.2024.11.013
Additional Keywords:	Artificial Intelligence; Machine learning; Conservation; Biodiversity; Delphi
Date made live:	20 Dec 2024 09:36 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537823

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.tree.2024.11.013

Additional Keywords:

Artificial Intelligence; Machine learning; Conservation; Biodiversity; Delphi

Date made live:

20 Dec 2024 09:36 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/537823